Journal Article10.1557/JMR.2015.80
Generalized machine learning technique for automatic phase attribution in time variant high-throughput experimental studies
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TL;DR: In this paper, a machine learning algorithm was used to identify oxide phase growth during a high-throughput oxidation study of NiAl bond coats that used x-ray diffraction, Raman, and fluorescence spectroscopic techniques.
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Abstract: Phase identification is an arduous task during high-throughput processing experiments, which can be exacerbated by the need to reconcile results from multiple measurement techniques to form a holistic understanding of phase dynamics. Here, we demonstrate AutoPhase, a machine learning algorithm, which can identify the presence of the different phases in spectral and diffraction data. The algorithm uses training data to determine the characteristic features of each phase present and then uses these features to evaluate new spectral and diffraction data. AutoPhase was used to identify oxide phase growth during a high-throughput oxidation study of NiAl bond coats that used x-ray diffraction, Raman, and fluorescence spectroscopic techniques. The algorithm had a minimum overall accuracy of 88.9% for unprocessed data and 98.4% for postprocessed data. Although the features selected by AutoPhase for phase attribution were distinct from those of topical experts, these results show that AutoPhase can substantially increase the throughput high-throughput data analysis.
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Citations
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
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TL;DR: In this article, a machine learning-enabled approach was proposed to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns using simulated data from the ICSD and experimental data.
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
Rama K. Vasudevan,Kamal Choudhary,Apurva Mehta,Ryan P. Smith,Gilad Kusne,Francesca Tavazza,Lukas Vlcek,Maxim Ziatdinov,Sergei V. Kalinin,Jason R. Hattrick-Simpers +9 more
TL;DR: Recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales is reviewed, showing how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework.
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167
A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns.
TL;DR: A facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds is reported.
Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge
TL;DR: In this paper, the authors review the field of automated phase diagram attribution and discuss the impact that emerging computational approaches will have in the generation of phase diagrams and beyond, as well as the impact of computational approaches on phase diagram generation.
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